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Mean +- std dev: 4.22 us +- 0.08 us  Or write a benchmark script ``bench.py``:  .. code:: python      #!/usr/bin/env python3     import perf      runner = perf.Runner()     runner.timeit(\"sorted(list(range(1000)), key=lambda x: x)\",                   stmt=\"sorted(s, key=f)\",                   setup=\"f = lambda x: x; s = list(range(1000))\")  Run the script using (result written into ``bench.json``)::      $ python3 bench.py -o bench.json  To `analyze benchmark results`_ use the ``perf stats`` command::      $ python3 -m perf stats bench.json     Total duration: 29.2 sec     Start date: 2016-10-21 03:14:19     End date: 2016-10-21 03:14:53     Raw value minimum: 177 ms     Raw value maximum: 183 ms      Number of calibration run: 1     Number of run with values: 40     Total number of run: 41      Number of warmup per run: 1     Number of value per run: 3     Loop iterations per value: 8     Total number of values: 120      Minimum:         22.1 ms     Median +- MAD:   22.5 ms +- 0.1 ms     Mean +- std dev: 22.5 ms +- 0.2 ms     Maximum:         22.9 ms        0th percentile: 22.1 ms (-2% of the mean) -- minimum       5th percentile: 22.3 ms (-1% of the mean)      25th percentile: 22.4 ms (-1% of the mean) -- Q1      50th percentile: 22.5 ms (-0% of the mean) -- median      75th percentile: 22.7 ms (+1% of the mean) -- Q3      95th percentile: 22.9 ms (+2% of the mean)     100th percentile: 22.9 ms (+2% of the mean) -- maximum      Number of outlier (out of 22.0 ms..23.0 ms): 0  There's also:  * ``perf compare_to`` command tests if a difference is   significant. It supports comparison between multiple benchmark suites (made   of multiple benchmarks)   ::      $ python3 -m perf compare_to py2.json py3.json --table     +-----------+---------+------------------------------+     | Benchmark | py2     | py3                          |     +===========+=========+==============================+     | timeit    | 4.70 us | 4.22 us: 1.11x faster (-10%) |     +-----------+---------+------------------------------+  * ``perf system tune`` command to tune your system to run stable benchmarks. * Automatically collect metadata on the computer and the benchmark:   use the ``perf metadata`` command to display them, or the   ``perf collect_metadata`` command to manually collect them. * ``--track-memory`` and ``--tracemalloc`` options to track   the memory usage of a benchmark.   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This logic allows for clients to express a preference for how the server allocates its (limited) resources to the many outstanding HTTP requests that may be running over a single HTTP/2 connection.  Specifically, this Python implementation uses a variant of the implementation used in the excellent `H2O`_ project. This original implementation is also the inspiration for `nghttp2's`_ priority implementation, and generally produces a very clean and even priority stream. The only notable changes from H2O's implementation are small modifications to allow the priority implementation to work cleanly as a separate implementation, rather than being embedded in a HTTP/2 stack directly.  While priority information in HTTP/2 is only a suggestion, rather than an enforceable constraint, where possible servers should respect the priority requests of their clients.  Using Priority --------------  Priority has a simple API. Streams are inserted into the tree: when they are inserted, they may optionally have a weight, depend on another stream, or become an exclusive dependent of another stream.  .. code-block:: python      >>> p = priority.PriorityTree()     >>> p.insert_stream(stream_id=1)     >>> p.insert_stream(stream_id=3)     >>> p.insert_stream(stream_id=5, depends_on=1)     >>> p.insert_stream(stream_id=7, weight=32)     >>> p.insert_stream(stream_id=9, depends_on=7, weight=8)     >>> p.insert_stream(stream_id=11, depends_on=7, exclusive=True)  Once streams are inserted, the stream priorities can be requested. This allows the server to make decisions about how to allocate resources.  Iterating The Tree ~~~~~~~~~~~~~~~~~~  The tree in this algorithm acts as a gate. Its goal is to allow one stream \"through\" at a time, in such a manner that all the active streams are served as evenly as possible in proportion to their weights.  This is handled in Priority by iterating over the tree. The tree itself is an iterator, and each time it is advanced it will yield a stream ID. This is the ID of the stream that should next send data.  This looks like this:  .. code-block:: python      >>> for stream_id in p:     ...     send_data(stream_id)  If each stream only sends when it is 'ungated' by this mechanism, the server will automatically be emitting stream data in conformance to RFC 7540.  Updating The Tree ~~~~~~~~~~~~~~~~~  If for any reason a stream is unable to proceed (for example, it is blocked on HTTP/2 flow control, or it is waiting for more data from another service), that stream is *blocked*. The ``PriorityTree`` should be informed that the stream is blocked so that other dependent streams get a chance to proceed. This can be done by calling the ``block`` method of the tree with the stream ID that is currently unable to proceed. This will automatically update the tree, and it will adjust on the fly to correctly allow any streams that were dependent on the blocked one to progress.  For example:  .. code-block:: python      >>> for stream_id in p:     ...     send_data(stream_id)     ...     if blocked(stream_id):     ...         p.block(stream_id)  When a stream goes from being blocked to being unblocked, call the ``unblock`` method to place it back into the sequence. Both the ``block`` and ``unblock`` methods are idempotent and safe to call repeatedly.  Additionally, the priority of a stream may change. When it does, the ``reprioritize`` method can be used to update the tree in the wake of that change. ``reprioritize`` has the same signature as ``insert_stream``, but applies only to streams already in the tree.  Removing Streams ~~~~~~~~~~~~~~~~  A stream can be entirely removed from the tree by calling ``remove_stream``. Note that this is not idempotent. Further, calling ``remove_stream`` and then re-adding it *may* cause a substantial change in the shape of the priority tree, and *will* cause the iteration order to change.  License -------  Priority is made available under the MIT License. For more details, see the LICENSE file in the repository.  Authors -------  Priority is maintained by Cory Benfield, with contributions from others. For more details about the contributors, please see CONTRIBUTORS.rst in the repository.   .. _RFC 7540 Section 5.3 (Stream Priority): https://tools.ietf.org/html/rfc7540#section-5.3 .. _nghttp2's: https://nghttp2.org/blog/2015/11/11/stream-scheduling-utilizing-http2-priority/ .. _H2O: https://h2o.examp1e.net/   Changelog =========  1.3.0 (2017-01-27) ------------------  **API Changes**  - Throw ``PriorityLoop`` when inserting or reprioritising a stream that   depends on itself. - Throw ``BadWeightError`` when creating or reprioritising a stream with a   weight that is not an integer between 1 and 256, inclusive. - Throw ``PseudoStreamError`` when trying to reprioritise, remove, block or   unblock stream 0. - Add a new ``PriorityError`` parent class for the exceptions that can be   thrown by priority.  1.2.2 (2016-11-11) ------------------  **Bugfixes**  - Allow ``insert_stream`` to be called with ``exclusive=True`` but no explicit   ``depends_on`` value.  1.2.1 (2016-10-26) ------------------  **Bugfixes**  - Allow insertion of streams that have parents in the idle or closed states.   This would previously raise a KeyError.  1.2.0 (2016-08-04) ------------------  **Security Fixes**  - CVE-2016-6580: All versions of this library prior to 1.2.0 are vulnerable to   a denial of service attack whereby a remote peer can cause a user to insert   an unbounded number of streams into the priority tree, eventually consuming   all available memory.    This version adds a ``TooManyStreamsError`` exception that is raised when   too many streams are inserted into the priority tree. It also adds a keyword   argument to the priority tree, ``maximum_streams``, which limits how many   streams may be inserted. By default, this number is set to 1000.   Implementations should strongly consider whether they can set this value   lower.  1.1.1 (2016-05-28) ------------------  **Bugfixes**  - 2.5x performance improvement by swapping from ``queue.PriorityQueue`` to   ``heapq``.  1.1.0 (2016-01-08) ------------------  **API Changes**  - Throw ``DuplicateStreamError`` when inserting a stream that is already in the   tree. - Throw ``MissingStreamError`` when reprioritising a stream that is not in the   tree.  1.0.0 (2015-12-07) ------------------  - Initial release.","name":"priority"},"progress":{"versions":[{"artifact":"progress-1.4-py27_0.tar.bz2","download_url":"https://repo.anaconda.com/pkgs/main/linux-32/progress-1.4-py27_0.tar.bz2","number":"1.4","original_license":"ISC","published_at":"2018-06-25 19:57:28 +0000","dependencies":["python >=2.7,<2.8.0a0"],"arch":"linux-32","channel":"main"},{"artifact":"progress-1.5-py27_0.tar.bz2","download_url":"https://repo.anaconda.com/pkgs/main/linux-64/progress-1.5-py27_0.tar.bz2","number":"1.5","original_license":"ISC","published_at":"2019-03-13 01:36:54 +0000","dependencies":["python >=2.7,<2.8.0a0"],"arch":"linux-64","channel":"main"},{"artifact":"progress-1.3-py27hcb558ea_0.tar.bz2","download_url":"https://repo.anaconda.com/pkgs/main/win-64/progress-1.3-py27hcb558ea_0.tar.bz2","number":"1.3","original_license":"ISC","published_at":"2017-09-25 19:45:25 +0000","dependencies":["python >=2.7,<2.8.0a0"],"arch":"win-64","channel":"main"}],"repository_url":"https://github.com/verigak/progress","homepage":"https://github.com/verigak/progress","licenses":"ISC","description":null,"name":"progress"},"progressbar2":{"versions":[{"artifact":"progressbar2-3.34.3-py27h4679deb_0.tar.bz2","download_url":"https://repo.anaconda.com/pkgs/main/linux-32/progressbar2-3.34.3-py27h4679deb_0.tar.bz2","number":"3.34.3","original_license":"BSD-3-Clause","published_at":"2017-11-29 19:32:20 +0000","dependencies":["pytest-runner >=2.8","python >=2.7,<2.8.0a0","python-utils"],"arch":"linux-32","channel":"main"},{"artifact":"progressbar2-3.35.2-py27_0.tar.bz2","download_url":"https://repo.anaconda.com/pkgs/main/linux-32/progressbar2-3.35.2-py27_0.tar.bz2","number":"3.35.2","original_license":"BSD-3-Clause","published_at":"2018-02-26 19:36:31 +0000","dependencies":["pytest-runner >=2.8","python >=2.7,<2.8.0a0","python-utils","six"],"arch":"linux-32","channel":"main"},{"artifact":"progressbar2-3.36.0-py27_0.tar.bz2","download_url":"https://repo.anaconda.com/pkgs/main/linux-32/progressbar2-3.36.0-py27_0.tar.bz2","number":"3.36.0","original_license":"BSD-3-Clause","published_at":"2018-04-03 20:00:42 +0000","dependencies":["pytest-runner >=2.8","python >=2.7,<2.8.0a0","python-utils","six"],"arch":"linux-32","channel":"main"},{"artifact":"progressbar2-3.37.1-py27_0.tar.bz2","download_url":"https://repo.anaconda.com/pkgs/main/linux-32/progressbar2-3.37.1-py27_0.tar.bz2","number":"3.37.1","original_license":"BSD-3-Clause","published_at":"2018-04-18 20:13:50 +0000","dependencies":["pytest-runner >=2.8","python >=2.7,<2.8.0a0","python-utils","six"],"arch":"linux-32","channel":"main"}],"repository_url":"https://github.com/WoLpH/python-progressbar","homepage":"https://github.com/WoLpH/python-progressbar","licenses":"BSD-3-Clause","description":"A text progress bar is typically used to display the progress of a long running operation, providing a visual cue that processing is underway.  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>=2.7,<2.8.0a0"],"arch":"linux-32","channel":"main"}],"repository_url":null,"homepage":"The package home page","licenses":"Apache Software License or BSD","description":"***************************************************************** testscenarios: extensions to python unittest to support scenarios *****************************************************************    Copyright (c) 2009, Robert Collins <robertc@robertcollins.net>      Licensed under either the Apache License, Version 2.0 or the BSD 3-clause   license at the users choice. A copy of both licenses are available in the   project source as Apache-2.0 and BSD. You may not use this file except in   compliance with one of these two licences.      Unless required by applicable law or agreed to in writing, software   distributed under these licenses is distributed on an \"AS IS\" BASIS, WITHOUT   WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  See the   license you chose for the specific language governing permissions and   limitations under that license.   testscenarios provides clean dependency injection for python unittest style tests. This can be used for interface testing (testing many implementations via a single test suite) or for classic dependency injection (provide tests with dependencies externally to the test code itself, allowing easy testing in different situations).  Dependencies ============  * Python 2.6+ * testtools <https://launchpad.net/testtools>   Why TestScenarios =================  Standard Python unittest.py provides on obvious method for running a single test_foo method with two (or more) scenarios: by creating a mix-in that provides the functions, objects or settings that make up the scenario. This is however limited and unsatisfying. Firstly, when two projects are cooperating on a test suite (for instance, a plugin to a larger project may want to run the standard tests for a given interface on its implementation), then it is easy for them to get out of sync with each other: when the list of TestCase classes to mix-in with changes, the plugin will either fail to run some tests or error trying to run deleted tests. Secondly, its not as easy to work with runtime-created-subclasses (a way of dealing with the aforementioned skew) because they require more indirection to locate the source of the test, and will often be ignored by e.g. pyflakes pylint etc.  It is the intent of testscenarios to make dynamically running a single test in multiple scenarios clear, easy to debug and work with even when the list of scenarios is dynamically generated.   Defining Scenarios ==================  A **scenario** is a tuple of a string name for the scenario, and a dict of parameters describing the scenario.  The name is appended to the test name, and the parameters are made available to the test instance when it's run.  Scenarios are presented in **scenario lists** which are typically Python lists but may be any iterable.   Getting Scenarios applied =========================  At its heart the concept is simple. For a given test object with a list of scenarios we prepare a new test object for each scenario. This involves:  * Clone the test to a new test with a new id uniquely distinguishing it. * Apply the scenario to the test by setting each key, value in the scenario   as attributes on the test object.  There are some complicating factors around making this happen seamlessly. These factors are in two areas:  * Choosing what scenarios to use. (See Setting Scenarios For A Test). * Getting the multiplication to happen.   Subclasssing ++++++++++++  If you can subclass TestWithScenarios, then the ``run()`` method in TestWithScenarios will take care of test multiplication. It will at test execution act as a generator causing multiple tests to execute. For this to  work reliably TestWithScenarios must be first in the MRO and you cannot override run() or __call__. This is the most robust method, in the sense that any test runner or test loader that obeys the python unittest protocol will run all your scenarios.  Manual generation +++++++++++++++++  If you cannot subclass TestWithScenarios (e.g. because you are using TwistedTestCase, or TestCaseWithResources, or any one of a number of other useful test base classes, or need to override run() or __call__ yourself) then  you can cause scenario application to happen later by calling ``testscenarios.generate_scenarios()``. For instance::    >>> import unittest   >>> try:   ...     from StringIO import StringIO   ... except ImportError:   ...     from io import StringIO   >>> from testscenarios.scenarios import generate_scenarios  This can work with loaders and runners from the standard library, or possibly other implementations::    >>> loader = unittest.TestLoader()   >>> test_suite = unittest.TestSuite()   >>> runner = unittest.TextTestRunner(stream=StringIO())    >>> mytests = loader.loadTestsFromNames(['doc.test_sample'])   >>> test_suite.addTests(generate_scenarios(mytests))   >>> runner.run(test_suite)   <unittest...TextTestResult run=1 errors=0 failures=0>  Testloaders +++++++++++  Some test loaders support hooks like ``load_tests`` and ``test_suite``. Ensuring your tests have had scenario application done through these hooks can be a good idea - it means that external test runners (which support these hooks like ``nose``, ``trial``, ``tribunal``) will still run your scenarios. (Of course, if you are using the subclassing approach this is already a surety). With ``load_tests``::    >>> def load_tests(standard_tests, module, loader):   ...     result = loader.suiteClass()   ...     result.addTests(generate_scenarios(standard_tests))   ...     return result  as a convenience, this is available in ``load_tests_apply_scenarios``, so a module using scenario tests need only say ::    >>> from testscenarios import load_tests_apply_scenarios as load_tests  Python 2.7 and greater support a different calling convention for `load_tests`` <https://bugs.launchpad.net/bzr/+bug/607412>.  `load_tests_apply_scenarios` copes with both.  With ``test_suite``::    >>> def test_suite():   ...     loader = TestLoader()   ...     tests = loader.loadTestsFromName(__name__)   ...     result = loader.suiteClass()   ...     result.addTests(generate_scenarios(tests))   ...     return result   Setting Scenarios for a test ============================  A sample test using scenarios can be found in the doc/ folder.  See `pydoc testscenarios` for details.  On the TestCase +++++++++++++++  You can set a scenarios attribute on the test case::    >>> class MyTest(unittest.TestCase):   ...   ...     scenarios = [   ...         ('scenario1', dict(param=1)),   ...         ('scenario2', dict(param=2)),]  This provides the main interface by which scenarios are found for a given test. Subclasses will inherit the scenarios (unless they override the attribute).  After loading +++++++++++++  Test scenarios can also be generated arbitrarily later, as long as the test has not yet run. Simply replace (or alter, but be aware that many tests may share a single scenarios attribute) the scenarios attribute. For instance in this example some third party tests are extended to run with a custom scenario. ::    >>> import testtools   >>> class TestTransport:   ...     \"\"\"Hypothetical test case for bzrlib transport tests\"\"\"   ...     pass   ...   >>> stock_library_tests = unittest.TestLoader().loadTestsFromNames(   ...     ['doc.test_sample'])   ...   >>> for test in testtools.iterate_tests(stock_library_tests):   ...     if isinstance(test, TestTransport):   ...         test.scenarios = test.scenarios + [my_vfs_scenario]   ...   >>> suite = unittest.TestSuite()   >>> suite.addTests(generate_scenarios(stock_library_tests))  Generated tests don't have a ``scenarios`` list, because they don't normally require any more expansion.  However, you can add a ``scenarios`` list back on to them, and then run them through ``generate_scenarios`` again to generate the cross product of tests. ::    >>> class CrossProductDemo(unittest.TestCase):   ...     scenarios = [('scenario_0_0', {}),   ...                  ('scenario_0_1', {})]   ...     def test_foo(self):   ...         return   ...   >>> suite = unittest.TestSuite()   >>> suite.addTests(generate_scenarios(CrossProductDemo(\"test_foo\")))   >>> for test in testtools.iterate_tests(suite):   ...     test.scenarios = [   ...         ('scenario_1_0', {}),    ...         ('scenario_1_1', {})]   ...   >>> suite2 = unittest.TestSuite()   >>> suite2.addTests(generate_scenarios(suite))   >>> print(suite2.countTestCases())   4  Dynamic Scenarios +++++++++++++++++  A common use case is to have the list of scenarios be dynamic based on plugins and available libraries. An easy way to do this is to provide a global scope scenarios somewhere relevant to the tests that will use it, and then that can be customised, or dynamically populate your scenarios from a registry etc. For instance::    >>> hash_scenarios = []   >>> try:   ...     from hashlib import md5   ... except ImportError:   ...     pass   ... else:   ...     hash_scenarios.append((\"md5\", dict(hash=md5)))   >>> try:   ...     from hashlib import sha1   ... except ImportError:   ...     pass   ... else:   ...     hash_scenarios.append((\"sha1\", dict(hash=sha1)))   ...   >>> class TestHashContract(unittest.TestCase):   ...   ...     scenarios = hash_scenarios   ...   >>> class TestHashPerformance(unittest.TestCase):   ...   ...     scenarios = hash_scenarios   Forcing Scenarios +++++++++++++++++  The ``apply_scenarios`` function can be useful to apply scenarios to a test that has none applied. ``apply_scenarios`` is the workhorse for ``generate_scenarios``, except it takes the scenarios passed in rather than introspecting the test object to determine the scenarios. The ``apply_scenarios`` function does not reset the test scenarios attribute, allowing it to be used to layer scenarios without affecting existing scenario selection.   Generating Scenarios ====================  Some functions (currently one :-) are available to ease generation of scenario lists for common situations.  Testing Per Implementation Module +++++++++++++++++++++++++++++++++  It is reasonably common to have multiple Python modules that provide the same capabilities and interface, and to want apply the same tests to all of them.  In some cases, not all of the statically defined implementations will be able to be used in a particular testing environment.  For example, there may be both a C and a pure-Python implementation of a module.  You want to test the C module if it can be loaded, but also to have the tests pass if the C module has not been compiled.  The ``per_module_scenarios`` function generates a scenario for each named module. The module object of the imported module is set in the supplied attribute name of the resulting scenario. Modules which raise ``ImportError`` during import will have the ``sys.exc_info()`` of the exception set instead of the module object. Tests can check for the attribute being a tuple to decide what to do (e.g. to skip).  Note that for the test to be valid, all access to the module under test must go through the relevant attribute of the test object.  If one of the implementations is also directly imported by the test module or any other, testscenarios will not magically stop it being used.   Advice on Writing Scenarios ===========================  If a parameterised test is because of a bug run without being parameterized, it should fail rather than running with defaults, because this can hide bugs.   Producing Scenarios ===================  The `multiply_scenarios` function produces the cross-product of the scenarios passed in::    >>> from testscenarios.scenarios import multiply_scenarios   >>>    >>> scenarios = multiply_scenarios(   ...      [('scenario1', dict(param1=1)), ('scenario2', dict(param1=2))],   ...      [('scenario2', dict(param2=1))],   ...      )   >>> scenarios == [('scenario1,scenario2', {'param2': 1, 'param1': 1}),   ...               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